Primary exercises
- Dietary intakes. (Create a vector, use it in
calculation.)
Four patients had daily dietary intakes of 2314, 2178, 1922, 2004
kcal.
Make a vector intakesKCal of these four values.
What is the class of this vector?
Convert the values into in kJ using 1 kcal = 4.184 kJ.
intakesKCal <- c( 2314, 2178, 1922, 2004 )
intakesKCal
[1] 2314 2178 1922 2004
class( intakesKCal )
[1] "numeric"
intakesKCal * 4.184
[1] 9681.776 9112.752 8041.648 8384.736
- More dietary intakes. (Combining/appending/merging
vectors.)
Additional set of intakes is provided: 2122, 2616, NA, 1771 kcal.
Use c() to append the new intakes after values in
intakesKCal and store the result in
allIntakesKCal.
Print the combined vector and print its calculated
length.
intakesKCal2 <- c( 2122, 2616, NA, 1771 )
allIntakesKCal <- c( intakesKCal, intakesKCal2 )
allIntakesKCal
[1] 2314 2178 1922 2004 2122 2616 NA 1771
length( allIntakesKCal )
[1] 8
- The average and total intakes. (Calculating means and sums,
skipping missing values.)
Calculate mean intake for patients in vector
intakesKCal.
Next, calculate mean intake for patients in vector
allIntakesKCal.
Can you explain the result?
Check help for ?mean, in particular the na.rm
argument.
Use the extra argument na.rm=TRUE to calculate the
mean of non-NA elements of
allIntakesKCal.
Check help for ?sum how to omit NA elements in
sum calculation.
Now, calculate the total sum of allIntakesKCal
intakes ignoring the NA element.
mean( intakesKCal )
[1] 2104.5
mean( allIntakesKCal )
[1] NA
# since one element is missing, the mean is unknown
# ?mean, adding argument na.rm=TRUE will omit NA elements
mean( allIntakesKCal, na.rm = TRUE )
[1] 2132.429
# ?sum also allows na.rm=TRUE argument to skip NA elements
sum( allIntakesKCal, na.rm = TRUE )
[1] 14927
- Selecting valid intakes. (Selecting non-missing elements;
logical vectors.)
Understand the result of is.na( allIntakesKCal ).
Now, negate the above result with ! operator.
Use above vectors as argument to sum to calculate the
number of missing and non-missing elements in
allIntakesKCal.
Understand allIntakesKCal[ !is.na( allIntakesKCal ) ].
is.na( allIntakesKCal ) # TRUE marks positions with missing data
[1] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
!is.na( allIntakesKCal ) # TRUE marks positions with available data
[1] TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE
sum( is.na( allIntakesKCal ) ) # number of missing elements
[1] 1
sum( !is.na( allIntakesKCal ) ) # number of non-missing elements
[1] 7
allIntakesKCal[ !is.na( allIntakesKCal ) ] # keeps elements which are not NA
[1] 2314 2178 1922 2004 2122 2616 1771
sum( allIntakesKCal[ !is.na( allIntakesKCal ) ] ) # same as sum( allIntakesKCal, na.rm = TRUE )
[1] 14927
- Generating random kcal intakes. (Generating normally distributed
random numbers; descriptive statistics.)
The code v <- rnorm( 10 ) would sample 10 numbers from
the normal distribution and store them as a vector in
v.
Print v. Then repeat v <- rnorm( 10 ) and
print v again. Has v changed?
Next, read the manual of rnorm and find how to generate
random numbers with given mean and standard deviation
(sd).
Now, in v simulate kcal intake by generating 15 random
numbers with mean=2000 and sd=300.
Print v and find by eye the smallest and the largest of
these numbers.
Try to use the functions min and max on
v – have you found the same numbers by eye?
Calculate the mean, median and the standard
deviation (sd) of v.
v <- rnorm( 10 ) # a vector of random numbers
v
[1] 0.28724076 -0.60849399 0.65541794 0.23552369 0.45162409 -1.37408339 1.41123828 0.02561384 1.00225643 -1.41678828
v <- rnorm( 10 ) # another vector of random numbers
v
[1] -0.20261345 0.10220385 1.52160811 0.21963317 1.01972546 0.08614393 0.22797155 -1.71004032 2.02408856 -0.67838541
v <- rnorm( n = 15, mean = 2000, sd = 300 )
v
[1] 2386.026 2314.071 1813.312 2055.684 2354.977 2394.627 1601.707 1883.642 2568.902 1893.144 1972.670 1696.012 2059.417 1889.492 2003.888
min( v )
[1] 1601.707
max( v )
[1] 2568.902
mean( v ) # is it close to 2000? try several random v vectors and see the effect of growing n
[1] 2059.172
median( v )
[1] 2003.888
sd( v ) # is it close to 300? try several random v vectors and see the effect of growing n
[1] 284.2268
- Selecting and counting some kcal intakes. (Selecting elements by
a condition; logical vectors.)
In v simulate kcal intake by generating 15 random numbers
with mean=2000 and sd=300.
Type v < 2000 and understand the result.
How to interpret the number produced by
sum( v < 2000 )?
How to interpret the number produced by
sum( !( v < 2000 ) )?
v <- rnorm( n = 15, mean = 2000, sd = 300 )
v
[1] 2150.300 2191.134 2648.656 1791.419 1903.656 1667.783 1899.522 1526.354 1643.369 1737.205 2209.447 1931.474 1856.647 2455.187 1621.891
v < 2000 # TRUE corresponds to elements of vector v SMALLER THAN 2000
[1] FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE FALSE TRUE
v[ v < 2000 ] # selected elements of v smaller than 2000
[1] 1791.419 1903.656 1667.783 1899.522 1526.354 1643.369 1737.205 1931.474 1856.647 1621.891
sum( v < 2000 ) # number of elements in vector v smaller than 2000
[1] 10
sum( !( v < 2000 ) ) # number of elements in vector v GREATER OR EQUAL than 2000
[1] 5
sum( v >= 2000 ) # same as above
[1] 5
- Head and tail.
Often there is a need to quickly look at the beginning
(head) or at the end (tail) of a vector.
Try these functions to show the first 5 and the last 7 elements of a
randomly generated vector v <- rnorm( 20 ).
v <- rnorm( 20 )
v
[1] 0.581553521 -0.229232889 -1.109467881 -1.430286379 0.835967870 -2.012206589 -1.356088084 -1.567881769 -1.293854073 0.136677867 -1.237521206 -0.466882264
[13] 0.590789536 -1.420584233 0.334043139 1.970041224 0.657992628 1.346871632 0.004360538 -1.512186176
head( v, 5 )
[1] 0.5815535 -0.2292329 -1.1094679 -1.4302864 0.8359679
tail( v, 7 )
[1] -1.420584233 0.334043139 1.970041224 0.657992628 1.346871632 0.004360538 -1.512186176
- Elements of a vector.
Let’s assume that eight persons had caloric intakes of 2122, 2616, NA,
1771, 2314, 2178, 1922, 2004 kcal.
Make a vector intakesKCal of these eight values (in the
given order).
Use the square brackets to get the 4th element of
intakesKCal.
Use the square brackets and the colon operator (:) to get
the elements from the second to the fifth (inclusive).
Define another vector poses with values 1, 3, 5, 7. Use it
get the 1st, 3rd, 5th and 7th element of intakesKCal.
Finally, get the 1st, 3rd, 5th and 7th element of
intakesKCal typing numbers directly inside
[...] (without using an extra poses
variable).
intakesKCal <- c( 2122, 2616, NA, 1771, 2314, 2178, 1922, 2004 )
intakesKCal
[1] 2122 2616 NA 1771 2314 2178 1922 2004
intakesKCal[ 4 ]
[1] 1771
intakesKCal[ 2:5 ]
[1] 2616 NA 1771 2314
poses <- c(1,3,5,7)
intakesKCal[ poses ]
[1] 2122 NA 2314 1922
intakesKCal[ c(1,3,5,7) ]
[1] 2122 NA 2314 1922